data mining fourth edition practical machine learning tools and techniques morgan kaufmann series in data management systems

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Data Mining

Author : Ian H. Witten
ISBN : 9780128043578
Genre : Computers
File Size : 54. 88 MB
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Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book

Data Mining Practical Machine Learning Tools And Techniques

Author : Ian H. Witten
ISBN : 9780080890364
Genre : Computers
File Size : 35. 82 MB
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Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining

Author : Ian H. Witten
ISBN : 008047702X
Genre : Computers
File Size : 20. 67 MB
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Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses. Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods Performance improvement techniques that work by transforming the input or output

Data Mining Concepts And Techniques

Author : Jiawei Han
ISBN : 0123814804
Genre : Computers
File Size : 67. 99 MB
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Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Exploratory Data Mining And Data Cleaning

Author : Tamraparni Dasu
ISBN : 9780471458647
Genre : Mathematics
File Size : 58. 80 MB
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Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.

Data Preparation For Data Mining

Author : Dorian Pyle
ISBN : 1558605290
Genre : Computers
File Size : 81. 93 MB
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A guide to the importance of well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance, and provides examples of how to apply a variety of techniques in order to solve real world business problems.

Business Modeling And Data Mining

Author : Dorian Pyle
ISBN : 0080500455
Genre : Computers
File Size : 36. 80 MB
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Business Modeling and Data Mining demonstrates how real world business problems can be formulated so that data mining can answer them. The concepts and techniques presented in this book are the essential building blocks in understanding what models are and how they can be used practically to reveal hidden assumptions and needs, determine problems, discover data, determine costs, and explore the whole domain of the problem. This book articulately explains how to understand both the strategic and tactical aspects of any business problem, identify where the key leverage points are and determine where quantitative techniques of analysis -- such as data mining -- can yield most benefit. It addresses techniques for discovering how to turn colloquial expression and vague descriptions of a business problem first into qualitative models and then into well-defined quantitative models (using data mining) that can then be used to find a solution. The book completes the process by illustrating how these findings from data mining can be turned into strategic or tactical implementations. · Teaches how to discover, construct and refine models that are useful in business situations · Teaches how to design, discover and develop the data necessary for mining · Provides a practical approach to mining data for all business situations · Provides a comprehensive, easy-to-use, fully interactive methodology for building models and mining data · Provides pointers to supplemental online resources, including a downloadable version of the methodology and software tools.

Instant Weka How To

Author : Boštjan Kaluža
ISBN : 9781782163879
Genre : Computers
File Size : 45. 4 MB
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Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. A practical guide with examples and applications of programming Weka in Java.This book primarily targets Java developers who want to build Weka's data mining capabilities into their projects. Computer science students, data scientists, artificial intelligence programmers, and statistical programmers would equally gain from this book and would learn about essential tasks required to implement a project. Experience with Weka concepts is assumed.

Security For Web Services And Service Oriented Architectures

Author : Elisa Bertino
ISBN : 9783540877424
Genre : Computers
File Size : 87. 83 MB
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Web services technologies are advancing fast and being extensively deployed in many di?erent application environments. Web services based on the eXt- sible Markup Language (XML), the Simple Object Access Protocol (SOAP), andrelatedstandards,anddeployedinService-OrientedArchitectures(SOAs) are the key to Web-based interoperability for applications within and across organizations. Furthermore, they are making it possible to deploy appli- tions that can be directly used by people, and thus making the Web a rich and powerful social interaction medium. The term Web 2.0 has been coined to embrace all those new collaborative applications and to indicate a new, “social” approach to generating and distributing Web content, characterized by open communication, decentralization of authority, and freedom to share and reuse. For Web services technologies to hold their promise, it is crucial that - curity of services and their interactions with users be assured. Con?dentiality, integrity,availability,anddigitalidentitymanagementareallrequired.People need to be assured that their interactions with services over the Web are kept con?dential and the privacy of their personal information is preserved. People need to be sure that information they use for looking up and selecting s- vicesiscorrectanditsintegrityisassured.Peoplewantservicestobeavailable when needed. They also require interactions to be convenient and person- ized, in addition to being private. Addressing these requirements, especially when dealing with open distributed applications, is a formidable challenge.

Data Mining Techniques

Author : Gordon S. Linoff
ISBN : 1118087453
Genre : Computers
File Size : 86. 61 MB
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The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised— is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.

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